Demo Data from EDBT 2026 / reviewers in the wild / expert
Seth R. Johnson
dblp:174/8791
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2025
0000-0003-1504-8966ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Geometric modeling and processing · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Geometric modeling and processing › solid modeling
constructive solid geometry |
0.9 | 1 | 2025 | Point containment algorithms for constructive solid geometry with unbounded primitives · Comput. Aided Des. 2025 |
Geometric modeling and processing › computational geometry › containment queries
point inclusion |
0.9 | 1 | 2025 | Point containment algorithms for constructive solid geometry with unbounded primitives · Comput. Aided Des. 2025 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Point containment algorithms for constructive solid geometry with unbounded primitives
Paul K. Romano, Patrick A. Myers, Seth R. Johnson, Aljaz Kolsek, Patrick C. Shriwise |
Comput. Aided Des. | 3 |
| 2025 | A cross-platform execution engine for the quantum intermediate representationabstractHybrid languages like the quantum intermediate representation (QIR) are essential for programming systems that mix quantum and conventional computing models, while execution of these programs is often deferred to a system-specific implementation. Here, we develop the QIR Execution Engine (QIR-EE) for parsing, interpreting, and executing QIR across multiple hardware platforms. QIR-EE uses LLVM to execute hybrid instructions specifying quantum programs and, by design, presents extension points that support customized runtime and hardware environments. We demonstrate an implementation that uses the XACC quantum hardware-accelerator library to dispatch prototypical quantum programs on different commercial quantum platforms and numerical simulators, and we validate execution of QIR-EE on IonQ, Quantinuum, and IBM hardware. Our results highlight the efficiency of hybrid executable architectures for handling mixed instructions, managing mixed data, and integrating with quantum computing frameworks to realize cross-platform execution. Vicente Leyton-Ortega, Daniel Claudino, Seth R. Johnson, Austin J. Adams, Sharmin Afrose, Meenambika Gowrishankar, Anthony M. Cabrera, Travis S. Humble |
J. Supercomput. | 4 |